/home/angelos/stock-analysis/venv_39/lib/python3.9/site-packages/gluonts/json.py:45: UserWarning: Using `json`-module for json-handling. Consider installing one of `orjson`, `ujson` to speed up serialization and deserialization.
  warnings.warn(

Download Alphavantage data and format

BTC 2020-08-20 00:00:00 2021-12-01 00:00:00
ETH 2020-08-20 00:00:00 2021-12-01 00:00:00
ADA 2020-08-20 00:00:00 2021-12-01 00:00:00
DOT 2020-08-20 00:00:00 2021-12-01 00:00:00
SOL 2020-08-20 00:00:00 2021-12-01 00:00:00

Define DeepAR params

	Group
BTC	1
ETH	1
ADA	2
DOT	2
SOL	2

Training

100%|██████████| 50/50 [00:02<00:00, 17.31it/s, epoch=1/100, avg_epoch_loss=4.61]
100%|██████████| 50/50 [00:02<00:00, 24.99it/s, epoch=2/100, avg_epoch_loss=3.81]
100%|██████████| 50/50 [00:02<00:00, 24.83it/s, epoch=3/100, avg_epoch_loss=3.57]
100%|██████████| 50/50 [00:02<00:00, 24.72it/s, epoch=4/100, avg_epoch_loss=3.35]
100%|██████████| 50/50 [00:01<00:00, 25.04it/s, epoch=5/100, avg_epoch_loss=3.24]
100%|██████████| 50/50 [00:02<00:00, 24.19it/s, epoch=6/100, avg_epoch_loss=3.21]
100%|██████████| 50/50 [00:02<00:00, 24.06it/s, epoch=7/100, avg_epoch_loss=3.07]
100%|██████████| 50/50 [00:01<00:00, 25.02it/s, epoch=8/100, avg_epoch_loss=3.27]
100%|██████████| 50/50 [00:02<00:00, 23.84it/s, epoch=9/100, avg_epoch_loss=3.38]
100%|█████████| 50/50 [00:02<00:00, 21.66it/s, epoch=10/100, avg_epoch_loss=2.99]
100%|█████████| 50/50 [00:02<00:00, 22.84it/s, epoch=11/100, avg_epoch_loss=3.24]
100%|█████████| 50/50 [00:02<00:00, 23.34it/s, epoch=12/100, avg_epoch_loss=3.05]
100%|█████████| 50/50 [00:02<00:00, 22.60it/s, epoch=13/100, avg_epoch_loss=3.13]
100%|█████████| 50/50 [00:02<00:00, 22.48it/s, epoch=14/100, avg_epoch_loss=3.01]
100%|█████████| 50/50 [00:02<00:00, 21.14it/s, epoch=15/100, avg_epoch_loss=3.01]
100%|██████████| 50/50 [00:02<00:00, 24.21it/s, epoch=16/100, avg_epoch_loss=3.1]
100%|█████████| 50/50 [00:02<00:00, 24.91it/s, epoch=17/100, avg_epoch_loss=2.94]
100%|██████████| 50/50 [00:02<00:00, 23.80it/s, epoch=18/100, avg_epoch_loss=2.9]
100%|█████████| 50/50 [00:02<00:00, 24.00it/s, epoch=19/100, avg_epoch_loss=3.08]
100%|█████████| 50/50 [00:02<00:00, 22.20it/s, epoch=20/100, avg_epoch_loss=2.96]
100%|█████████| 50/50 [00:02<00:00, 24.46it/s, epoch=21/100, avg_epoch_loss=3.04]
100%|█████████| 50/50 [00:02<00:00, 20.76it/s, epoch=22/100, avg_epoch_loss=2.92]
100%|█████████| 50/50 [00:02<00:00, 24.19it/s, epoch=23/100, avg_epoch_loss=3.14]
100%|█████████| 50/50 [00:02<00:00, 22.02it/s, epoch=24/100, avg_epoch_loss=3.15]
100%|█████████| 50/50 [00:02<00:00, 22.59it/s, epoch=25/100, avg_epoch_loss=3.18]
100%|█████████| 50/50 [00:02<00:00, 21.06it/s, epoch=26/100, avg_epoch_loss=2.89]
100%|██████████| 50/50 [00:02<00:00, 20.09it/s, epoch=27/100, avg_epoch_loss=2.9]
100%|█████████| 50/50 [00:02<00:00, 21.51it/s, epoch=28/100, avg_epoch_loss=2.89]
100%|█████████| 50/50 [00:02<00:00, 20.49it/s, epoch=29/100, avg_epoch_loss=2.95]
100%|█████████| 50/50 [00:02<00:00, 22.53it/s, epoch=30/100, avg_epoch_loss=3.04]
100%|█████████| 50/50 [00:02<00:00, 18.79it/s, epoch=31/100, avg_epoch_loss=2.82]
100%|██████████| 50/50 [00:02<00:00, 19.14it/s, epoch=32/100, avg_epoch_loss=2.9]
100%|█████████| 50/50 [00:02<00:00, 17.03it/s, epoch=33/100, avg_epoch_loss=3.11]
100%|█████████| 50/50 [00:02<00:00, 17.19it/s, epoch=34/100, avg_epoch_loss=2.97]
100%|█████████| 50/50 [00:02<00:00, 17.58it/s, epoch=35/100, avg_epoch_loss=2.99]
100%|█████████| 50/50 [00:02<00:00, 21.33it/s, epoch=36/100, avg_epoch_loss=2.93]
100%|█████████| 50/50 [00:02<00:00, 23.70it/s, epoch=37/100, avg_epoch_loss=2.88]
100%|████████████| 50/50 [00:02<00:00, 21.33it/s, epoch=38/100, avg_epoch_loss=3]
100%|█████████| 50/50 [00:02<00:00, 24.30it/s, epoch=39/100, avg_epoch_loss=3.07]
100%|█████████| 50/50 [00:02<00:00, 24.17it/s, epoch=40/100, avg_epoch_loss=2.81]
100%|█████████| 50/50 [00:02<00:00, 24.35it/s, epoch=41/100, avg_epoch_loss=2.88]
100%|█████████| 50/50 [00:02<00:00, 24.52it/s, epoch=42/100, avg_epoch_loss=2.89]
100%|█████████| 50/50 [00:02<00:00, 24.30it/s, epoch=43/100, avg_epoch_loss=2.96]
100%|█████████| 50/50 [00:02<00:00, 22.60it/s, epoch=44/100, avg_epoch_loss=2.95]
100%|█████████| 50/50 [00:03<00:00, 16.05it/s, epoch=45/100, avg_epoch_loss=3.06]
100%|█████████| 50/50 [00:02<00:00, 20.09it/s, epoch=46/100, avg_epoch_loss=2.84]
100%|████████████| 50/50 [00:02<00:00, 24.87it/s, epoch=47/100, avg_epoch_loss=3]
100%|█████████| 50/50 [00:02<00:00, 23.18it/s, epoch=48/100, avg_epoch_loss=2.87]
100%|█████████| 50/50 [00:02<00:00, 22.04it/s, epoch=49/100, avg_epoch_loss=3.05]
100%|█████████| 50/50 [00:02<00:00, 22.78it/s, epoch=50/100, avg_epoch_loss=2.94]
100%|█████████| 50/50 [00:02<00:00, 22.55it/s, epoch=51/100, avg_epoch_loss=2.95]
100%|█████████| 50/50 [00:02<00:00, 22.80it/s, epoch=52/100, avg_epoch_loss=2.88]
100%|█████████| 50/50 [00:02<00:00, 19.02it/s, epoch=53/100, avg_epoch_loss=2.92]
100%|█████████| 50/50 [00:02<00:00, 19.14it/s, epoch=54/100, avg_epoch_loss=2.92]
100%|█████████| 50/50 [00:02<00:00, 20.42it/s, epoch=55/100, avg_epoch_loss=2.91]
100%|█████████| 50/50 [00:02<00:00, 22.80it/s, epoch=56/100, avg_epoch_loss=3.01]
100%|█████████| 50/50 [00:02<00:00, 24.21it/s, epoch=57/100, avg_epoch_loss=2.95]
100%|█████████| 50/50 [00:02<00:00, 23.89it/s, epoch=58/100, avg_epoch_loss=2.83]
100%|█████████| 50/50 [00:02<00:00, 23.86it/s, epoch=59/100, avg_epoch_loss=2.86]
100%|█████████| 50/50 [00:02<00:00, 22.30it/s, epoch=60/100, avg_epoch_loss=3.05]
100%|█████████| 50/50 [00:02<00:00, 24.83it/s, epoch=61/100, avg_epoch_loss=3.01]
100%|█████████| 50/50 [00:02<00:00, 22.75it/s, epoch=62/100, avg_epoch_loss=3.06]
100%|█████████| 50/50 [00:02<00:00, 24.07it/s, epoch=63/100, avg_epoch_loss=2.89]
100%|█████████| 50/50 [00:02<00:00, 18.54it/s, epoch=64/100, avg_epoch_loss=3.01]
100%|█████████| 50/50 [00:03<00:00, 15.06it/s, epoch=65/100, avg_epoch_loss=2.92]
100%|█████████| 50/50 [00:02<00:00, 20.56it/s, epoch=66/100, avg_epoch_loss=3.11]
100%|█████████| 50/50 [00:02<00:00, 22.25it/s, epoch=67/100, avg_epoch_loss=3.17]
100%|█████████| 50/50 [00:03<00:00, 14.86it/s, epoch=68/100, avg_epoch_loss=2.94]
100%|█████████| 50/50 [00:02<00:00, 16.81it/s, epoch=69/100, avg_epoch_loss=3.06]
100%|█████████| 50/50 [00:03<00:00, 16.21it/s, epoch=70/100, avg_epoch_loss=2.97]

Early stopping based on learning rate scheduler callback (min_lr was reached).

Backward Forecasting / Evaluate

Running evaluation: 100%|██████████████████████████| 5/5 [00:00<00:00, 94.87it/s]

Till 2021-12-01 00:00:00: 0.18509858449300132
Till 2021-12-01 00:00:00:
0    0.148121
1    0.142132
2    0.176353
3    0.213240
4    0.245648
Name: MAPE, dtype: float64

Running evaluation: 100%|██████████████████████████| 5/5 [00:00<00:00, 94.79it/s]

Till 2021-11-16 00:00:00: 0.06959509611129762
Till 2021-11-16 00:00:00:
0    0.082152
1    0.070401
2    0.034415
3    0.094190
4    0.066818
Name: MAPE, dtype: float64

Running evaluation: 100%|██████████████████████████| 5/5 [00:00<00:00, 89.25it/s]

Till 2021-11-01 00:00:00: 0.04735976616541544
Till 2021-11-01 00:00:00:
0    0.025373
1    0.019523
2    0.028719
3    0.035322
4    0.127861
Name: MAPE, dtype: float64

Plot predictions during evaluation

Forward forecasting